Employ analytics to identify substandard supplier materials
What if your suppliers delivered materials, parts, components, and assemblies that consistently met your quality requirements? For many manufacturers this is the ideal scenario for which they strive, though rarely achieve. Cost of quality varies per industry and manufacturer, and ASQ statistics indicate there is ample evidence for further improvement.
Dan Jacob’s of LNS Research post – 5 Maneuvers to Drive a Culture of Quality [Infographic] succinctly characterizes the state of supplier quality management (SQM) by providing key statistics, a maturity scale (ad-hoc to innovation leader) for assessing SQM practices, and 5 tips to help progress toward maturity. The “culture of quality” concept mentioned in the infographic implies the cross-functional aspect of quality management throughout the supply chain to enable manufacturing organizations to – “transform quality from a department and bake it into your company’s culture.”
The infographic is based upon the LNS Research eBook – Supplier Quality Management: SQM’s Rightful Role In Your Enterprise which contains a wealth of analysis and insight relevant to any manufacturer wishing to further improve supplier quality management practices. Per the infographic, only 23% of companies identify as having a culture of quality. How would you rate your company?
The eBook acknowledges the growing interest in employing IIoT capabilities to improve quality initiatives and use of machine learning to reveal new insights, mentioning “this could mean applying advanced analytics on incoming inspection data to find previously undetectable trends in supplier defects, detect these trends more quickly, or even detect them at the supplier’s sites.”
Per the LNS eBook “A majority (57% of respondents) indicate they incorporate quality performance data in their enterprise analytics systems, and the most commonly gathered smart connected product data is quality inspection data.” While that statistic indicates that the practice is well beyond the early adoption stage, what of the remaining 43%?
IBM’s and analytics
Although IBM does not provide Supplier Quality Management solutions, long-term investments in analytics research applied to its own supply chain practices have resulted in development of IBM’s Quality Early Warning System (QEWS) algorithms. In comparison to many traditional statistical process control methods, IBM’s QEWS can detect impending quality problems earlier and more definitively, thereby helping to prevent substandard materials from entering the manufacturing process, reduce scrap and rework, improve throughput, and reduce warranty costs.
QEWS is now incorporated into IBM’s Prescriptive Quality on Cloud software which can be utilized to capture data whenever a test is performed, an inspection is done, or a measurement is made. It supports manufacturers’ need for monitoring quality metrics associated with materials, parts, components, assemblies, products and manufacturing processes. The prescriptive nature of the offering can help identify the root cause of the quality problem such as substandard materials, drift in equipment calibration, sub-standard assembly processes.
The eBook states that when statistical analysis is used to monitor and analyze real-time supplier quality data it can lead to a 43% reduction in median supplier defect rate. The advantage of employing IBM Prescriptive Quality on Cloud with its QEWS algorithms over existing SPC methods would be earlier detection of quality issues. One of the initial applications of IBM QEWS within its supply chain was monitoring supplier quality.
If supplier quality improvement is high-priority, we encourage you to attend the Supplier Quality Management’s Rightful Role in Your Enterprise: Realize the Total Value of Quality on January 11, 2017. If you are currently employing SPC methods (interestingly the LNS eBook notes that although SPC was ranked by 27% of respondents as a “quality process most critical to company’s success” however 44% of respondents mentioned it as one of the quality processes considered least mature) and are interested in earlier, more definitive detection of quality problems, please take a few minutes to consider how IBM Prescriptive Quality on Cloud might help improve your quality management practices.